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Pierre, K., Haneberg, A.G., Kwak, S., Peters, K.R., Hochhegger, B., Sananmuang, T., Tunlayadechanont, P., Tighe, P.J., Mancuso, A. and Forghani, R. (2023) Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond. Seminars in Roentgenology, 58, 158-169.
https://doi.org/10.1053/j.ro.2023.02.003

has been cited by the following article:

• TITLE: A Machine Learning-Based Web Application for Heart Disease Prediction

  AUTHORS: Jesse Gabriel

  KEYWORDS: Heart Disease, US Center for Disease Control and Prevention, Machine Learn-ing, Imbalanced Data, Web Application

  JOURNAL NAME: Intelligent Control and Automation, Vol.15 No.1, February 18, 2024

  ABSTRACT: This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.